Simulating Macroeconomic Expectations using LLM Agents
This work provides a novel framework for AI behavioral science in macroeconomic research, complementing traditional survey methods, though it is incremental in applying existing LLM technology to a specific domain.
The authors tackled the problem of simulating macroeconomic expectation formation by using LLM Agents to replicate survey experiments on inflation and unemployment, finding that the agents effectively captured key heterogeneity and drivers despite generating more homogeneous expectations than humans.
We introduce a novel framework for simulating macroeconomic expectation formation using Large Language Model-Empowered Agents (LLM Agents). By constructing thousands of LLM Agents equipped with modules for personal characteristics, prior expectations, and knowledge, we replicate a survey experiment involving households and experts on inflation and unemployment. Our results show that although the expectations and thoughts generated by LLM Agents are more homogeneous than those of human participants, they still effectively capture key heterogeneity across agents and the underlying drivers of expectation formation. Furthermore, a module-ablation exercise highlights the critical role of prior expectations in simulating such heterogeneity. This approach complements traditional survey methods and offers new insights into AI behavioral science in macroeconomic research.